Semi-supervised Learning Algorithm Based on Linear Lie Group for Imbalanced Multi-class Classification


In practical application, the data are imbalanced, it is difficult to find the balanced, rather skewed data is the common occurrence. This poses a severe challenge to the classification algorithm. At present, imbalanced data classification methods are mainly for binary classes designed, and it is difficult to extend them to multiple classes. In this study, we introduced Lie group machine learning and proposed a semi-supervised learning algorithm based on the linear Lie group. First, the sample set is represented by a matrix, the isomorphism(or homomorphism)-GL(n) linear Lie group of the corresponding learning system is found, and the labeled data are used to represent the object to be learned by linear Lie group. Then, according to the algebraic structure of the linear Lie group, it is marked by the group method. We performed experiments on 18 benchmark multi-class imbalanced datasets to demonstrate the performance of our proposed method and measured the performance of multi-class imbalanced data using four state-of-the-art learning algorithms (mean of accuracy, mean of f-measure, and mean of area under the curve). The experimental results demonstrate that the proposed method is effective and improves the performance.

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The authors would like to thank two anonymous reviewers for carefully reviewing this letter and giving valuable comments to improve this paper.

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Correspondence to Chengjun Xu.

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Xu, C., Zhu, G. Semi-supervised Learning Algorithm Based on Linear Lie Group for Imbalanced Multi-class Classification. Neural Process Lett (2020).

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  • Lie group
  • Lie group machine learning
  • Semi-supervised learning
  • Imbalanced data
  • Multi-class classification